{"id":"W2886266161","doi":"10.1038/s41467-018-05502-z","title":"Mapping the energy landscapes of supramolecular assembly by thermal hysteresis","year":2018,"lang":"en","type":"article","venue":"Nature Communications","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Government of Canada","keywords":"Supramolecular chemistry; Kinetics; Materials science; Self-assembly; Monomer; Energy landscape; Melting temperature; Macromolecule; Supramolecular assembly; Chemical physics; Polymer; Nanotechnology; Biological system; Chemistry; Crystallography; Crystal structure; Biology; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001994438,0.0001679455,0.0001366721,0.0004609267,0.0001834894,0.0003277727,0.0002298115,0.0002277745,0.0009795924],"category_scores_gemma":[0.0004774161,0.0002197985,0.0001184807,0.0002146152,0.0004010387,0.0004365459,0.0002077985,0.0003927001,0.0001148843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000268935,"about_ca_system_score_gemma":0.0001042418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002373052,"about_ca_topic_score_gemma":0.0003331834,"domain_scores_codex":[0.9999123,0.00001660726,0.000003929454,0.00002695681,0.00002585986,0.00001436023],"domain_scores_gemma":[0.9997402,0.0001535938,0.00004226731,0.00002731672,0.0000168486,0.00001972689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001257614,0.00005238816,0.002568568,0.00007901803,0.00002560532,0.0000541202,0.0001215934,0.008485305,0.9743024,0.002670306,0.0001076806,0.01140732],"study_design_scores_gemma":[0.00003463066,0.0002477694,0.02190522,0.00001809284,0.00002948693,0.0001775913,0.0001546827,0.2797688,0.6889232,0.006922563,0.00176783,0.00005000693],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9755316,0.0003302296,0.02235649,0.00004607803,0.000004697713,0.00001334538,0.00005852371,0.0001599567,0.001499134],"genre_scores_gemma":[0.9945025,0.0001237028,0.005053629,0.00001148229,0.000002810359,0.00002188419,0.00004599898,0.00002022309,0.0002178202],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009795924,"threshold_uncertainty_score":0.003277123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008417826885641825,"score_gpt":0.2519873196475691,"score_spread":0.2435694927619273,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}